def test_fracvol_predictor_with_acc_v2(dl_model, test_data, save_path):
# An attempt to reduce inference time as the v1 takes over 15 minutes per volume...
vol_saver_path = os.path.join(save_path, 'predicted_volumes')
if os.path.exists(vol_saver_path) is False:
os.mkdir(vol_saver_path)
for vol_index, each_vol in enumerate(test_data):
start_time = time.time()
# Load Nifti Volumes of Input, Output and Mask
input_vol = load_nifty(each_vol['input_image'], data_type='float32')
output_vol = load_nifty(each_vol['output_image'], data_type='float32')
mask_vol = load_nifty(each_vol['mask'], data_type='float32')
# Convert mask_vol to int to save space
mask_vol = np.int16(mask_vol)
vol_dims = mask_vol.shape
pred_vol = np.zeros((vol_dims[0], vol_dims[1], vol_dims[2], 48))
for x in range(vol_dims[0]):
print(x)
for y in range(vol_dims[1]):
for z in range(vol_dims[2]):
if mask_vol[x, y, z] == 1:
ip_voxel = np.squeeze(input_vol[x, y, z, :])
ip_voxel = np.reshape(ip_voxel, [1, 45])
t_pred = dl_model.predict(ip_voxel)
pred_vol[x, y, z, :] = np.squeeze(t_pred)
end_time = time.time()
time_taken = end_time - start_time
print('Predictions Completed for Vol {} & Time Taken was {} \n'.format(
vol_index, time_taken))
print('Saving predicted volume')
save_nifti(pred_vol, each_vol['output_image'], vol_saver_path)
print('Predicted Volume Saved ... \n ')
#### Calculate ACC
print('Calculating ACC')
acc_vol = np.zeros((vol_dims[0], vol_dims[1], vol_dims[2]))
for x in range(vol_dims[0]):
print(x)
for y in range(vol_dims[1]):
for z in range(vol_dims[2]):
if mask_vol[x, y, z] == 1:
op_voxel = np.squeeze(output_vol[x, y, z, :])
pred_voxel = np.squeeze(pred_vol[x, y, z, :])
acc_vol[x, y, z] = calc_acc_numpy(op_voxel, pred_voxel)
save_nifti_acc(acc_vol, each_vol['output_image'], vol_saver_path)
def test_save_csd_acc_v2(test_data, save_path):
# An attempt to reduce inference time as the v1 takes over 15 minutes per volume...
vol_saver_path = os.path.join(save_path, 'predicted_volumes')
if os.path.exists(vol_saver_path) is False:
os.mkdir(vol_saver_path)
batch_size = 1000
for vol_index, each_vol in enumerate(test_data):
start_time = time.time()
# Load Nifti Volumes of Input, Output and Mask
#input_vol = load_nifty(each_vol['input_image'], data_type='float32')
output_vol = load_nifty(each_vol['output_image'], data_type='float32')
csd_vol = load_nifty(each_vol['csd_img'], data_type='float32')
mask_vol = load_nifty(each_vol['mask'], data_type='float32')
# Convert mask_vol to int to save space
mask_vol = np.int16(mask_vol)
vol_dims = mask_vol.shape
#### Calculate ACC
print('Calculating ACC')
acc_vol = np.zeros((vol_dims[0], vol_dims[1], vol_dims[2]))
for x in range(vol_dims[0]):
print(x)
for y in range(vol_dims[1]):
for z in range(vol_dims[2]):
if mask_vol[x, y, z] == 1:
op_voxel = np.squeeze(output_vol[x, y, z, :])
pred_voxel = np.squeeze(csd_vol[x, y, z, :])
acc_vol[x, y, z] = calc_acc_numpy(op_voxel, pred_voxel)
save_nifti_acc(acc_vol, each_vol['output_image'], vol_saver_path)
